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Mass Spectrometry: Complex Analysis01:21

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data.

Alejandro Sánchez Brotons1, Jonatan O Eriksson2, Marcel Kwiatkowski1,3

  • 1Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, 9713 AV Groningen, The Netherlands.

Analytical Chemistry
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

The PASTAQ toolset enhances liquid chromatography-tandem mass spectrometry (LC-MS/MS) data processing for metabolomics and proteomics. It provides accurate quantification and feature detection, improving analysis of biological samples.

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Accurate processing of complex LC-MS/MS data is crucial but challenging for metabolomics and proteomics.
  • Existing tools often struggle with high-dimensional biological datasets.

Purpose of the Study:

  • To present the Pipelines and Systems for Threshold-Avoiding Quantification (PASTAQ) LC-MS/MS preprocessing toolset.
  • To enable highly accurate quantification of data-dependent acquisition LC-MS/MS datasets.

Main Methods:

  • PASTAQ utilizes single-stage (MS1) data for compound quantification.
  • Novel algorithms are implemented for retention time alignment, feature detection, and linking annotations.
  • The toolset offers straightforward parameterization and automatic quality control plot generation.

Main Results:

  • PASTAQ demonstrates smaller variance in analyzing proteome replicates compared to existing tools.
  • It enables detection of peptides over a larger dynamic concentration range.
  • The pipeline successfully identified gender-related proteins in a human serum dataset.

Conclusions:

  • PASTAQ is a high-performance toolset for accurate LC-MS/MS data preprocessing.
  • It improves the analysis of complex biological samples in metabolomics and proteomics.
  • The toolset facilitates more robust identification and quantification of biological molecules.